Dash 2020

Row

Suchen in Primo
(im Vergleich zum Vorjahr 2019)

+16 %

Besuche der Website
(im Vergleich zum Vorjahr 2019)

+28 %

Ausleihe von Büchern
(im Vergleich zum Vorjahr 2019)

-50 %

Row

Downloads aus E-Books von Springer
(im Vergleich zum Vorjahr 2019)

+13 %

Downloads aus E-Journals von Springer
(im Vergleich zum Vorjahr 2019)

+25 %

Row

Primo

Column

Primo A

Column

Primo B

Suchen in Primo
Year Search_Total Percent_Change
2018 164620 NA
2019 203863 0.2383854
2020 236113 0.1581945

Website

Column

Chart A

Chart B

Column

Chart C

Chart D

Ausleihen

Column

Ausleihen

# A tibble: 14 x 6
   Jahr  Ausleihen Verlängerungen Ausleihen_u_Verl `aktive Nutzer`
   <chr>     <dbl>          <dbl>            <dbl>           <dbl>
 1 2007     146921         988378          1135299            9372
 2 2008     149055        1178243          1327298            9221
 3 2009     145757        1239340          1385097            9047
 4 2010     128611        1082681          1211292            8316
 5 2011     123750         933680          1057430            8069
 6 2012     121487         905753          1027240            7975
 7 2013     109422         723648           833070            7904
 8 2014     101813         613201           715014            7621
 9 2015      91986         545428           637414            7818
10 2016      83387         469837           553224            7715
11 2017      77640         419573           497213              NA
12 2018      65929         365714           431643            5426
13 2019      57115         303461           360576            6468
14 2020      23430         158343           181773              NA
# ... with 1 more variable: Percent_Change <dbl>
---
title: "Nutzungsstatistiken 2020"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
 #   self_contained: false 
    # https://stackoverflow.com/questions/54643852/how-to-reduce-the-size-of-flexdashboards
---

 


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      message = FALSE,
                      warning = FALSE,
                      options(scipen = 999))

library(flexdashboard)
library(readxl)
library(tidyverse)
library(lubridate)
library(gt)
library(janitor)
library(scales)
#library(DT)
library(plotly)
library(gameofthrones)

# Ressources:
# https://towardsdatascience.com/building-an-hr-dashboard-in-r-using-flexdashboard-76d14ed3f32
# https://rmarkdown.rstudio.com/flexdashboard/using.html

```

Dash 2020 {data-icon="fa-globe"}
===================================== 

Row {data-width=150, data-height=200}
-----------------------------------------------------------------------

### Suchen in Primo 
(im Vergleich zum Vorjahr 2019) ```{r} articles <- "+16 %" valueBox(articles, icon = "fa-search-plus", color = "#36AFAEFF", href = "#primo") ``` ### Besuche der Website
(im Vergleich zum Vorjahr 2019) ```{r} comments <- "+28 %" valueBox(comments, icon = "fa-user-check", color = "#36AFAEFF", href = "#website") ``` ### Ausleihe von Büchern
(im Vergleich zum Vorjahr 2019) ```{r} spam <- "-50 %" valueBox(spam, icon = "fa-book", color = "#747374FF", href="#ausleihen") ``` ```{r eval=FALSE} ### #Die Zahl bezieht sich auf die prozentuale Veränderung zum Vorjahr 2019. ``` Row {data-width=150, data-height=200} ----------------------------------------------------------------------- ### Downloads aus E-Books von Springer
(im Vergleich zum Vorjahr 2019) ```{r} articles <- "+13 %" valueBox(articles, icon = "fa-search-plus", color = "#36AFAEFF", href = "#primo") ``` ### Downloads aus E-Journals von Springer
(im Vergleich zum Vorjahr 2019) ```{r} articles <- "+25 %" valueBox(articles, icon = "fa-search-plus", color = "#36AFAEFF", href = "#primo") ``` Row ----------------------------------------------------------------------- Primo {data-orientation=columns} ===================================== Column {data-width=400, data-height=400} ----------------------------------------------------------------------- ### Primo A ```{r} Primo_Stat_Auswertungen <- read_excel( "T:/Statistik/ALMA_ART/Primo_Stat_Auswertungen.xlsx", sheet = "R_1", col_types = c("date", "text", "numeric") ) ``` ```{r} Primo <- Primo_Stat_Auswertungen %>% mutate(Year = year(Date)) %>% group_by(Year, Action) %>% summarise(Value = sum(Value)) ``` ```{r} Primo_3 <- Primo %>% summarise(Search_Total = sum(Value)) %>% mutate(Percent_Change = (Search_Total / (lag(Search_Total)) - 1)) ``` ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Primo, aes(x = Year, y = Value)) + geom_col(aes(fill = Action), position = "dodge") + theme_classic() + labs( title = "Primo: Anzahl Suchen von 2018 bis 2020", subtitle = "", fill = "Suchen", y = "", x = "" ) + scale_fill_manual(values = pal) + theme(legend.position = "bottom") ggplotly(p1) %>% layout(legend = list(orientation = "h", y = -0.1)) #+ # facet_wrap(~Date) ``` Column {data-width=200, data-height=200} ----------------------------------------------------------------------- ### Primo B ```{r} Primo_2 <- Primo %>% spread(key = Year, value = Value) %>% adorn_totals("row") ``` ```{r} gt(Primo_3) %>% tab_header(title = md("Suchen in Primo")) ``` Website {data-orientation=columns} ===================================== Column ----------------------------------------------------------------------- ### Chart A ```{r} Website_Besuche <- read_excel( "T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Besuche", col_types = c("numeric", "text", "numeric") ) ``` ```{r} Website_Besuche_2 <- Website_Besuche %>% group_by(Action) %>% # filter(Action == "Besuche") %>% mutate(Percent_Change = (Value / (lag(Value)) - 1)) ``` ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Website_Besuche, aes(x = Date, y = Value)) + geom_col(aes(fill = Action), position = "dodge") + theme_classic() + scale_fill_manual(values = pal) + labs(title = "Website: Besuche und Seitenansichten 2018 bis 2020", fill = "", y = "", x = "") ggplotly(p1) ``` ### Chart B ```{r} Website_OS <- read_excel( "T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Desktop", col_types = c("text", "text", "numeric") ) ``` ```{r} pal <- got(3, option = "Jon_Snow", direction = -1) p1 <- Website_OS %>% filter(date == "2020") %>% plot_ly(marker = list(colors = pal)) %>% add_pie(labels = Website_OS$action, values = Website_OS$value, hole = 0.6) %>% layout(title = "Website: Verwendung Betriebssysteme 2020") p1 ``` Column ----------------------------------------------------------------------- ### Chart C ```{r} Website_Users <- read_excel( "T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Users", col_types = c("date", "numeric", "numeric") ) pal <- got(3, option = "Jon_Snow", direction = 1) ``` ```{r} Website_Users_2 <- Website_Users %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) p2 <- ggplot(Website_Users_2, aes(x = month, y = visits, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = visits, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + labs( title = "Website: Besuche 2018 bis 2020", y = "Besuche", x = "", color = "" ) ggplotly(p2) ``` ### Chart D ```{r} Website_Users_2 <- Website_Users %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) #%>% # filter(date >= "2019-01-01") p1 <- ggplot(Website_Users_2, aes(x = month, y = unique_users, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = unique_users, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + labs( title = "Website: Unique Users 2018 bis 2020", y = "Unique Users", x = "", color = "" ) ggplotly(p1) ``` Ausleihen {data-orientation=columns} ===================================== Column {data-width=400, data-height=400} ----------------------------------------------------------------------- ### Ausleihen ```{r} Ausleihen <- read_excel( "T:/Statistik/ALMA_ART/Ausleihen.xlsx", sheet = "Ausleihen_R", col_types = c("text", "numeric", "numeric", "numeric", "numeric"), na = "NA" ) ``` ```{r} pal <- got(12, option = "Jon_Snow", direction = -1) p1 <- ggplot(Ausleihen, aes(x = Jahr, y = Ausleihen_u_Verl)) + geom_col(fill = pal[1]) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs( title = "Ausleihen alle Standorte 2007 bis 2020", y = "Ausleihen", x = "", color = "" ) ggplotly(p1) ``` ```{r} Ausleihen %>% mutate(Percent_Change = (Ausleihen_u_Verl / (lag(Ausleihen_u_Verl)) - 1)) ```